Expression recognition based on residual rectifier enhanced convolution neural network

In order to solve the problem of low face recognition rate in controlled scene, an expression recognition algorithm based on residual rectification enhanced convolutional neural network is proposed. This method takes convolutional neural network as the prototype. In the process of training model, the idea of residual network is introduced to correct the difference between the effect of test set and the effect of training set. The linear rectification operation of the residual block by the excitation function embedded in the convolution layer helps to express complex features. At the same time, the data enhancement method is used to suppress the fast fitting of the deep neural network model during the training process, and also to improve its generalization performance on a given recognition task, and then to improve the robustness of the model learning effect. In the experiment, the method is applied to simulate the online teaching environment, and the effect of effective facial expression recognition in controlled scene is achieved. According to the experimental data, this method can effectively classify the facial image input under controlled conditions, and the highest accuracy is up to 91.7%. This research is helpful to the development of facial expression recognition and human-computer interaction.